Solutions for SAP®

Data quality for SAP business partner data

Successful delivery of your goods. Good customer communication. 360° view of your business partners. A successful migration to SAP S/4HANA. Reliable identification of fraud attempts. For all these processes, you need up-to-date, correct, consistent and unique business partner data.

We have an SAP development team in-house. 
Benefit from our experience.


What do you want to achieve within your SAP solution? Realize your goals - without compromise. With the help of our SAP experts, you will increase the business value of your data, optimize your technology platform and increase the ROI of your SAP investment. Our in-house SAP development team has unparalleled SAP expertise and decades of industry experience. We work with you to consider what's possible, plan your solution, drive user adoption, and assist with change management - at every stage of your SAP projects.


We can support you in these and many other project scenarios:

  • Postal address validation
  • Extensive duplicate check
  • Data analysis and data migration
  • Implementation of long-term data quality strategies
  • Defense against fraud attempts and implementation of compliance regulations
  • Migration of business partner data in optimized quality to SAP S/4HANA


"Implementing individual requirements in the SAP environment that offer our customers real added value - that's our specialty."

Marc Schober, Senior Developer-Consultant SAP, Uniserv GmbH





How can I measure data quality?

Here we recommend thinking about meaningful KPIs. This is often not so easy and can quickly get out of hand. We have the expertise to do this in-house, we can make data quality visible and measure it. We know the crucial KPIs and can give tips on how to increase data quality. 

It is precisely the step with the appropriate KPIs that is crucial, because normally you only have to tackle the issue of data quality once on a large scale and install the appropriate mechanisms. However, the data must then also be continuously checked, improved and protected. 

What thoughts do I need to think about before making a switch?

Before migration, it should be checked in any case which data is actually needed, i.e. which data system is needed to support processes. A clear overview of the entire data landscape helps to establish clearly defined key figures and thus to be able to make decisions based on data. Therefore, especially in the case of a migration, one should consider which data is still necessary at all and which may no longer be needed.

What happens if I disregard data quality as part of a migration?

Problems occur in the new system. Processes do not run as desired, bad data is taken over from the legacy systems and thus hinders the processes in the new system. Due to the digitization of processes, it is essential that the data in the systems is correct. If the changeover is rushed, the data quality is often ignored and bad data is taken over. Bad data, however, leads to problems during migration and in the new system, data is not as it should be and problems then have to be worked through very laboriously afterwards.

Do I need to permanently adjust my data or is a one-time cleanup enough?

It is often assumed that a one-time cleanup, e.g. as part of a migration, is sufficient. However, data is subject to permanent change. Take, for example, the data field address. Through street renaming, incorporation, etc., an address changes over time. If the data is then cleaned only once - at great expense and effort - without regular data quality mechanisms, problems will arise again in a few years because the data will become outdated again. This again results in a high effort combined with high costs.  

Permanent backup mechanisms are useful, for example, to check data directly as it is entered. It also makes sense to integrate mechanisms when transferring data between systems.

In the best case, therefore, regular data updates are introduced to track changes and reduce costs and effort. Then the system is always "fit for use" and the data is suitable for the processes.